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Search : [ author: 김도경 ] (2)

Document-level Machine Translation Data Augmentation Using a Cluster Algorithm and NSP

Dokyoung Kim, Changki Lee

http://doi.org/10.5626/JOK.2023.50.5.401

In recent years, research on document level machine translation has been actively conducted to understand the context of the entire document and perform natural translation. Similar to the sentence-level machine translation model, a large amount of training data is required for training of the document-level machine translation model, but there is great difficulty in building a large amount of document-level parallel corpus. Therefore, in this paper, we propose a data augmentation technique effective for document-level machine translation in order to improve the lack of parallel corpus per document. As a result of the experiment, by applying the data augmentation technique using the cluster algorithm and NSP to the sentence unit parallel corpus without context, the performance of the document-level machine translation is improved by S-BLEU 3.0 and D-BLEU 2.7 compared to that before application of the data augmentation technique.

Analysis of Speech Emotion Database and Development of Speech Emotion Recognition System using Attention Mechanism Integrating Frame- and Utterance-level Features

Dokyung Kim, Yoonjoong Kim

http://doi.org/10.5626/JOK.2020.47.5.479

In this study, we propose a model consist of BLSTM (Bidirectional Long-Sort Term Memory) layer, Attention mechanism layer, and Deep neural network to integrate frame- and utterance-level features from speech signals model reliability analysis the labels in the speech emotional database IEMOCAP (Interactive Emotional Dyadic Motion Capture). Based on the evaluation script of the labels provided in the IEMOCAP database, a default data set, a data set with a balanced distribution of emotion classes, and a data set with improved reliability based on three or more judgments were constructed and used for performance of the proposed model using speaker independent cross validation approach. Experiment on the improved and balanced dataset achieve a maximum score of 67.23% (WA, Weighted Accuracy) and 56.70% (UA, Unweighted Accuracy) that represents an improvement of 6.47% (WA), 4.41% (UA) over the baseline dataset.


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